Moving target detection and classification using spiking neural networks

Authors:
Rongtai Cai;Qingxiang Wu;Ping Wang;Honghai Sun;Zichen Wang
Affiliations:
School of Physics, Optics, Electronic and Information, Fujian Normal University, Fuzhou, Fujian, China;School of Physics, Optics, Electronic and Information, Fujian Normal University, Fuzhou, Fujian, China;School of Physics, Optics, Electronic and Information, Fujian Normal University, Fuzhou, Fujian, China;Changchun Institute of Optics, Fine mechanics and Physics, Chinese Academy of Science, Changchun, Jilin, China;Changchun Institute of Optics, Fine mechanics and Physics, Chinese Academy of Science, Changchun, Jilin, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

We proposed a spiking neural network (SNN) to detect moving target in video streams and classify them into real categorization in this paper. The proposed SNN uses spike trains to encoding information such as the gray value of pixels or feature parameters of the target, detects moving target by simulating the visual cortex for motion detection in biological system with axonal delays and classify them into different categorizations according to their distance to categorization's centers found by Hebb learning rule. The experimental results show that the proposed SNN is promising in intelligence computation and applicable in general visual surveillance system.